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Applying Machine Learning in Science Education Research : When, how, and why?
This open access textbook offers science education researchers a hands-on guide for learning, critically examining, and integrating machine learning (ML) methods into their science education research projects.These methods power many artificial intelligence (AI)-based technologies and are widely adopted in science education research.ML can expand the methodological toolkit of science education researchers and provide novel opportunities to gain insights on science-related learning and teaching processes, however, applying ML poses novel challenges and is not suitable for every research context. The volume first introduces the theoretical underpinnings of ML methods and their connections to methodological commitments in science education research.It then presents exemplar case studies of ML uses in both formal and informal science education settings.These case studies include open-source data, executable programming code, and explanations of the methodological criteria and commitments guiding ML use in each case.The textbook concludes with a discussion of opportunities and potential future directions for ML in science education. This textbook is a valuable resource for science education lecturers, researchers, under-graduate, graduate and postgraduate students seeking new ways to apply ML in their work.
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Gravitational Wave Science with Machine Learning
This book highlights the state of the art of machine learning applied to the science of gravitational waves.The main topics of the book range from the search for astrophysical gravitational wave signals to noise suppression techniques and control systems using machine learning-based algorithms.During the four years of work in the COST Action CA17137-A network for Gravitational Waves, Geophysics and Machine Learning (G2net), the collaboration produced several original publications as well as tutorials and lectures in the training schools we organized.The book encapsulates the immense amount of finding and achievements. It is a timely reference for young researchers approaching the analysis of data from gravitational wave experiments, with alternative approaches based on the use of artificial intelligence techniques.
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Machine Learning for Social and Behavioral Research
Today's social and behavioral researchers increasingly need to know: "What do I do with all this data?" This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles.Techniques are demonstrated using actual data (Big Five Inventory, early childhood learning, and more), with a focus on the interplay of statistical algorithm, data, and theory.The identification of heterogeneity, measurement error, regularization, and decision trees are also emphasized.The book covers basic principles as well as a range of methods for analyzing univariate and multivariate data (factor analysis, structural equation models, and mixed-effects models).Analysis of text and social network data is also addressed.End-of-chapter "Computational Time and Resources" sections include discussions of key R packages; the companion website provides R programming scripts and data for the book's examples.
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Encyclopedia of Data Science and Machine Learning
Big data and machine learning are driving the Fourth Industrial Revolution.With the age of big data upon us, we risk drowning in a flood of digital data.Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential.Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health.As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed.The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more.It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities.Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.
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Applied Machine Learning for Data Science Practitioners
Single volume reference on using various aspects of data science to evaluate, understand, and solve business problems A reference book for anyone in the field of data science, Applied Machine Learning for Data Science Practitioners walks readers through the end-to-end process of solving any machine learning problem by identifying, choosing, and applying the right solution for the issue at hand.The text enables readers to figure out optimal validation techniques based on the use case and data orientation, choose a range of pertinent models from different types of learning, and score models to apply metrics across all the estimators evaluated.Unlike most books on data science in today's market that jump right into algorithms and coding and focus on the most-used algorithms, this text helps data scientists evaluate all pertinent techniques and algorithms to assess all these machine learning problems and suitable solutions.Readers can make an informed decision on which models and validation techniques to use based on the business problem, data availability, desired outcome, and more.Written by an internationally recognized author in the field of data science, Applied Machine Learning for Data Science Practitioners also covers topics such as: Data preparation, including basic data cleaning, integration, transformation, and compression methods, along with data visualization and exploratory analysesCross-validation in model validation techniques, including independent, identically distributed, imbalanced, blocked, and grouped dataPrediction using regression models and classification using classification models, with applicable performance measurements for eachTypes of clustering in clustering models based on partition, hierarchy, fuzzy theory, distribution, density, and graph theoryDetecting anomalies, including types of anomalies and key terms like noise, rare events, and outliers Applied Machine Learning for Data Science Practitioners is an essential resource for all data scientists and business professionals to cross-validate a range of different algorithms to find an optimal solution.Readers are assumed to have a basic understanding of solving business problems using data, high school level math, statistics, and coding skills.
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Scripture Arabic Learning Machine Early Education Machine Explosive Tablet Pointing Machine Arabic
Scripture Arabic Learning Machine Early Education Machine Explosive Tablet Pointing Machine Arabic
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Scripture Arabic Learning Machine Early Education Machine Explosive Tablet Pointing Machine Arabic
Scripture Arabic Learning Machine Early Education Machine Explosive Tablet Pointing Machine Arabic
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Scripture Arabic Learning Machine Early Education Machine Explosive Tablet Pointing Machine Arabic
Scripture Arabic Learning Machine Early Education Machine Explosive Tablet Pointing Machine Arabic
Price: 2.05 £ | Shipping*: 1.99 £
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